CATA-optimize-to-ideal (CATA-OI): A consumer-driven product optimization method compared with liking/disliking CATA...

This study introduces Check-All-That-Apply Optimize-to-Ideal (CATA-OI), a novel consumer-based sensory method that integrates CATA profiling, hedonic assessment, and ideal product evaluation in a single session. When a sample does not match the consumer's ideal, consumer explicitly indicate which attributes should be added/increased, or reduced/removed, providing direct, sample-specific optimization guidance. The objective was to evaluate CATA-OI by comparing it with Liking/Disliking CATA and the Ideal Profile Method (IPM) using four Korean fermented soybean paste (doenjang) samples with distinct sensory profiles as a case study. In total, 157 female consumers participated in a between-subjects design: CATA-OI (n = 59), Liking/Disliking CATA (n = 48), and IPM (n = 50). All groups completed a shared questionnaire assessing the sensory attributes of ideal doenjang and overall liking of each sample, followed by method-specific evaluations. Umami, gu-su (holistic, savory flavor), salty, aged flavor were consistently identified as essential; hwa-han (tingly, warm sensation) texture, metallic, and ammonia flavor were undesirable. One of the commercially fermented samples scored highest for liking across all groups and fit-to-ideal in the CATA-OI group; traditionally handmade samples scored lowest. Liking/Disliking CATA identified affective drivers but lacked targeted improvement direction, while IPM highlighted perceived-to-ideal deviations without reflecting hedonic importance. CATA-OI fit-to-ideal scores closely aligned with overall liking rankings. Multiple Factor Analysis confirmed CATA-OI produced sensory configurations consistent with both IPM (RV = 0.977) and Liking/Disliking CATA (RV = 0.954), positioning it as a hybrid method combining IPM's profiling strength with CATA's consumer-driven accessibility. CATA-OI offers a practical and valid tool for deriving ideal profiles and sample-specific improvements, supporting consumer-oriented product optimization.